{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**目标：根据历史数据，预测当天股票最高价**\n",
    "\n",
    "**1. 改为用 30 天预测 7 天的股价**  \n",
    "**2. 测试只有前 30 天的数据，预测后面所有的数据**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模块导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-13T06:59:27.827455Z",
     "start_time": "2019-03-13T06:59:27.082418Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import os\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "from torch.utils.data import Dataset, DataLoader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据读取"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 原始数据获取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-13T06:59:28.611301Z",
     "start_time": "2019-03-13T06:59:27.830341Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "      <th>p_change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-01-04</th>\n",
       "      <td>000300</td>\n",
       "      <td>994.76</td>\n",
       "      <td>982.79</td>\n",
       "      <td>994.76</td>\n",
       "      <td>980.65</td>\n",
       "      <td>74128.0</td>\n",
       "      <td>4.431976e+09</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-05</th>\n",
       "      <td>000300</td>\n",
       "      <td>981.57</td>\n",
       "      <td>992.56</td>\n",
       "      <td>997.32</td>\n",
       "      <td>979.87</td>\n",
       "      <td>71191.0</td>\n",
       "      <td>4.529207e+09</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-06</th>\n",
       "      <td>000300</td>\n",
       "      <td>993.33</td>\n",
       "      <td>983.17</td>\n",
       "      <td>993.78</td>\n",
       "      <td>980.33</td>\n",
       "      <td>62880.0</td>\n",
       "      <td>3.921015e+09</td>\n",
       "      <td>-0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-07</th>\n",
       "      <td>000300</td>\n",
       "      <td>983.04</td>\n",
       "      <td>983.95</td>\n",
       "      <td>995.71</td>\n",
       "      <td>979.81</td>\n",
       "      <td>72986.0</td>\n",
       "      <td>4.737468e+09</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-10</th>\n",
       "      <td>000300</td>\n",
       "      <td>983.76</td>\n",
       "      <td>993.87</td>\n",
       "      <td>993.95</td>\n",
       "      <td>979.78</td>\n",
       "      <td>57916.0</td>\n",
       "      <td>3.762931e+09</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-11</th>\n",
       "      <td>000300</td>\n",
       "      <td>994.18</td>\n",
       "      <td>997.13</td>\n",
       "      <td>999.55</td>\n",
       "      <td>991.09</td>\n",
       "      <td>58490.0</td>\n",
       "      <td>3.704076e+09</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-12</th>\n",
       "      <td>000300</td>\n",
       "      <td>996.65</td>\n",
       "      <td>996.74</td>\n",
       "      <td>996.97</td>\n",
       "      <td>989.25</td>\n",
       "      <td>50145.0</td>\n",
       "      <td>3.093298e+09</td>\n",
       "      <td>-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-13</th>\n",
       "      <td>000300</td>\n",
       "      <td>996.07</td>\n",
       "      <td>996.87</td>\n",
       "      <td>999.47</td>\n",
       "      <td>992.69</td>\n",
       "      <td>60440.0</td>\n",
       "      <td>3.842172e+09</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-14</th>\n",
       "      <td>000300</td>\n",
       "      <td>996.61</td>\n",
       "      <td>988.30</td>\n",
       "      <td>1006.46</td>\n",
       "      <td>987.23</td>\n",
       "      <td>72978.0</td>\n",
       "      <td>4.162920e+09</td>\n",
       "      <td>-0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-17</th>\n",
       "      <td>000300</td>\n",
       "      <td>979.11</td>\n",
       "      <td>967.45</td>\n",
       "      <td>981.52</td>\n",
       "      <td>965.07</td>\n",
       "      <td>72881.0</td>\n",
       "      <td>4.249807e+09</td>\n",
       "      <td>-2.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-18</th>\n",
       "      <td>000300</td>\n",
       "      <td>967.37</td>\n",
       "      <td>974.68</td>\n",
       "      <td>974.87</td>\n",
       "      <td>960.29</td>\n",
       "      <td>73118.0</td>\n",
       "      <td>4.117944e+09</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-19</th>\n",
       "      <td>000300</td>\n",
       "      <td>974.33</td>\n",
       "      <td>967.21</td>\n",
       "      <td>974.33</td>\n",
       "      <td>965.25</td>\n",
       "      <td>63380.0</td>\n",
       "      <td>3.427951e+09</td>\n",
       "      <td>-0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-20</th>\n",
       "      <td>000300</td>\n",
       "      <td>963.21</td>\n",
       "      <td>956.24</td>\n",
       "      <td>963.21</td>\n",
       "      <td>952.23</td>\n",
       "      <td>77271.0</td>\n",
       "      <td>4.399349e+09</td>\n",
       "      <td>-1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-21</th>\n",
       "      <td>000300</td>\n",
       "      <td>954.46</td>\n",
       "      <td>982.60</td>\n",
       "      <td>984.27</td>\n",
       "      <td>943.43</td>\n",
       "      <td>144500.0</td>\n",
       "      <td>8.152086e+09</td>\n",
       "      <td>2.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-24</th>\n",
       "      <td>000300</td>\n",
       "      <td>1001.85</td>\n",
       "      <td>998.13</td>\n",
       "      <td>1001.85</td>\n",
       "      <td>986.23</td>\n",
       "      <td>143594.0</td>\n",
       "      <td>8.360160e+09</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-25</th>\n",
       "      <td>000300</td>\n",
       "      <td>995.63</td>\n",
       "      <td>997.77</td>\n",
       "      <td>997.95</td>\n",
       "      <td>985.23</td>\n",
       "      <td>98625.0</td>\n",
       "      <td>6.157022e+09</td>\n",
       "      <td>-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-26</th>\n",
       "      <td>000300</td>\n",
       "      <td>995.78</td>\n",
       "      <td>989.92</td>\n",
       "      <td>999.47</td>\n",
       "      <td>988.47</td>\n",
       "      <td>76632.0</td>\n",
       "      <td>4.719439e+09</td>\n",
       "      <td>-0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-27</th>\n",
       "      <td>000300</td>\n",
       "      <td>987.34</td>\n",
       "      <td>974.63</td>\n",
       "      <td>987.70</td>\n",
       "      <td>973.77</td>\n",
       "      <td>69453.0</td>\n",
       "      <td>4.094397e+09</td>\n",
       "      <td>-1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-28</th>\n",
       "      <td>000300</td>\n",
       "      <td>974.63</td>\n",
       "      <td>969.20</td>\n",
       "      <td>975.62</td>\n",
       "      <td>965.20</td>\n",
       "      <td>57488.0</td>\n",
       "      <td>3.280950e+09</td>\n",
       "      <td>-0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-31</th>\n",
       "      <td>000300</td>\n",
       "      <td>965.78</td>\n",
       "      <td>954.87</td>\n",
       "      <td>965.78</td>\n",
       "      <td>953.14</td>\n",
       "      <td>67887.0</td>\n",
       "      <td>3.863572e+09</td>\n",
       "      <td>-1.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              code     open   close     high     low       vol        amount  \\\n",
       "datetime                                                                       \n",
       "2005-01-04  000300   994.76  982.79   994.76  980.65   74128.0  4.431976e+09   \n",
       "2005-01-05  000300   981.57  992.56   997.32  979.87   71191.0  4.529207e+09   \n",
       "2005-01-06  000300   993.33  983.17   993.78  980.33   62880.0  3.921015e+09   \n",
       "2005-01-07  000300   983.04  983.95   995.71  979.81   72986.0  4.737468e+09   \n",
       "2005-01-10  000300   983.76  993.87   993.95  979.78   57916.0  3.762931e+09   \n",
       "2005-01-11  000300   994.18  997.13   999.55  991.09   58490.0  3.704076e+09   \n",
       "2005-01-12  000300   996.65  996.74   996.97  989.25   50145.0  3.093298e+09   \n",
       "2005-01-13  000300   996.07  996.87   999.47  992.69   60440.0  3.842172e+09   \n",
       "2005-01-14  000300   996.61  988.30  1006.46  987.23   72978.0  4.162920e+09   \n",
       "2005-01-17  000300   979.11  967.45   981.52  965.07   72881.0  4.249807e+09   \n",
       "2005-01-18  000300   967.37  974.68   974.87  960.29   73118.0  4.117944e+09   \n",
       "2005-01-19  000300   974.33  967.21   974.33  965.25   63380.0  3.427951e+09   \n",
       "2005-01-20  000300   963.21  956.24   963.21  952.23   77271.0  4.399349e+09   \n",
       "2005-01-21  000300   954.46  982.60   984.27  943.43  144500.0  8.152086e+09   \n",
       "2005-01-24  000300  1001.85  998.13  1001.85  986.23  143594.0  8.360160e+09   \n",
       "2005-01-25  000300   995.63  997.77   997.95  985.23   98625.0  6.157022e+09   \n",
       "2005-01-26  000300   995.78  989.92   999.47  988.47   76632.0  4.719439e+09   \n",
       "2005-01-27  000300   987.34  974.63   987.70  973.77   69453.0  4.094397e+09   \n",
       "2005-01-28  000300   974.63  969.20   975.62  965.20   57488.0  3.280950e+09   \n",
       "2005-01-31  000300   965.78  954.87   965.78  953.14   67887.0  3.863572e+09   \n",
       "\n",
       "            p_change  \n",
       "datetime              \n",
       "2005-01-04       NaN  \n",
       "2005-01-05      0.99  \n",
       "2005-01-06     -0.95  \n",
       "2005-01-07      0.08  \n",
       "2005-01-10      1.01  \n",
       "2005-01-11      0.33  \n",
       "2005-01-12     -0.04  \n",
       "2005-01-13      0.01  \n",
       "2005-01-14     -0.86  \n",
       "2005-01-17     -2.11  \n",
       "2005-01-18      0.75  \n",
       "2005-01-19     -0.77  \n",
       "2005-01-20     -1.13  \n",
       "2005-01-21      2.76  \n",
       "2005-01-24      1.58  \n",
       "2005-01-25     -0.04  \n",
       "2005-01-26     -0.79  \n",
       "2005-01-27     -1.54  \n",
       "2005-01-28     -0.56  \n",
       "2005-01-31     -1.48  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tushare as ts\n",
    "\n",
    "cons = ts.get_apis()\n",
    "df = ts.bar('000300', conn=cons, asset='INDEX', start_date='2002-01-01', end_date='')\n",
    "\n",
    "# 注意历史数据靠前\n",
    "df = df.sort_index(ascending=True)\n",
    "df.to_csv('sh.csv')\n",
    "\n",
    "# 可以看出，周末不进行交易\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-13T06:59:28.690083Z",
     "start_time": "2019-03-13T06:59:28.613957Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "      <th>p_change</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-01-04</th>\n",
       "      <td>300</td>\n",
       "      <td>994.76</td>\n",
       "      <td>982.79</td>\n",
       "      <td>994.76</td>\n",
       "      <td>980.65</td>\n",
       "      <td>74128.0</td>\n",
       "      <td>4.431976e+09</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-05</th>\n",
       "      <td>300</td>\n",
       "      <td>981.57</td>\n",
       "      <td>992.56</td>\n",
       "      <td>997.32</td>\n",
       "      <td>979.87</td>\n",
       "      <td>71191.0</td>\n",
       "      <td>4.529207e+09</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-06</th>\n",
       "      <td>300</td>\n",
       "      <td>993.33</td>\n",
       "      <td>983.17</td>\n",
       "      <td>993.78</td>\n",
       "      <td>980.33</td>\n",
       "      <td>62880.0</td>\n",
       "      <td>3.921015e+09</td>\n",
       "      <td>-0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-07</th>\n",
       "      <td>300</td>\n",
       "      <td>983.04</td>\n",
       "      <td>983.95</td>\n",
       "      <td>995.71</td>\n",
       "      <td>979.81</td>\n",
       "      <td>72986.0</td>\n",
       "      <td>4.737468e+09</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-10</th>\n",
       "      <td>300</td>\n",
       "      <td>983.76</td>\n",
       "      <td>993.87</td>\n",
       "      <td>993.95</td>\n",
       "      <td>979.78</td>\n",
       "      <td>57916.0</td>\n",
       "      <td>3.762931e+09</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            code    open   close    high     low      vol        amount  \\\n",
       "2005-01-04   300  994.76  982.79  994.76  980.65  74128.0  4.431976e+09   \n",
       "2005-01-05   300  981.57  992.56  997.32  979.87  71191.0  4.529207e+09   \n",
       "2005-01-06   300  993.33  983.17  993.78  980.33  62880.0  3.921015e+09   \n",
       "2005-01-07   300  983.04  983.95  995.71  979.81  72986.0  4.737468e+09   \n",
       "2005-01-10   300  983.76  993.87  993.95  979.78  57916.0  3.762931e+09   \n",
       "\n",
       "            p_change  \n",
       "2005-01-04       NaN  \n",
       "2005-01-05      0.99  \n",
       "2005-01-06     -0.95  \n",
       "2005-01-07      0.08  \n",
       "2005-01-10      1.01  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('sh.csv', index_col=0)\n",
    "df.index = list(map(lambda x:datetime.datetime.strptime(x, '%Y-%m-%d'), df.index))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-13T06:59:28.699382Z",
     "start_time": "2019-03-13T06:59:28.692561Z"
    }
   },
   "outputs": [],
   "source": [
    "def getData(df, column, train_end=-300, days_before=30, days_pred=7, return_all=True, generate_index=False):\n",
    "    series = df[column].copy()\n",
    "    \n",
    "    # 创建训练集\n",
    "    data = pd.DataFrame()\n",
    "    \n",
    "    # 准备天数\n",
    "    for i in range(days_before):\n",
    "        # 最后的 -days_before - days_pred 天只是用于预测值，预留出来\n",
    "        data['b%d' % i] = series.tolist()[i: -days_before - days_pred + i]\n",
    "    \n",
    "    # 预测天数\n",
    "    for i in range(days_pred):\n",
    "        data['y%d' % i] = series.tolist()[days_before + i: - days_pred + i]\n",
    "        \n",
    "    # 是否生成 index\n",
    "    if generate_index:\n",
    "        data.index = series.index[days_before:]\n",
    "        \n",
    "    train_data, val_data, test_data = data[:train_end-300], data[train_end-300:train_end], data[train_end:]\n",
    "                \n",
    "    if return_all:\n",
    "        return train_data, val_data, test_data, series, df.index.tolist()\n",
    "    \n",
    "    return train_data, val_data, test_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 创建 LSTM 层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-13T06:59:28.712889Z",
     "start_time": "2019-03-13T06:59:28.701639Z"
    }
   },
   "outputs": [],
   "source": [
    "class LSTM(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(LSTM, self).__init__()\n",
    "        \n",
    "        self.lstm = nn.LSTM(\n",
    "            input_size=1,   # 输入尺寸为 1，表示一天的数据\n",
    "            hidden_size=128,\n",
    "            num_layers=1, \n",
    "            batch_first=True)\n",
    "        \n",
    "        self.out = nn.Sequential(\n",
    "            nn.Linear(128, 1))\n",
    "        \n",
    "    def forward(self, x):\n",
    "        r_out, (h_n, h_c) = self.lstm(x, None)   # None 表示 hidden state 会用全 0 的 state\n",
    "        out = self.out(r_out[:, -7:, :])          # 取最后一天作为输出\n",
    "        \n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-13T06:59:28.727126Z",
     "start_time": "2019-03-13T06:59:28.715491Z"
    }
   },
   "outputs": [],
   "source": [
    "class TrainSet(Dataset):\n",
    "    def __init__(self, data):\n",
    "        self.data, self.label = data[:, :-7].float(), data[:, -7:].float()\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return self.data[index], self.label[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 超参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-13T06:59:28.742902Z",
     "start_time": "2019-03-13T06:59:28.729792Z"
    }
   },
   "outputs": [],
   "source": [
    "LR = 0.0001\n",
    "EPOCH = 1000\n",
    "TRAIN_END=-300\n",
    "DAYS_BEFORE=30\n",
    "DAYS_PRED=7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 获取数据\n",
    "- 训练模型仍然使用 minibatch 的思路\n",
    "- 注意，模型必须要先把数据标准化，不然损失会很难降低下来。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-13T06:59:29.085743Z",
     "start_time": "2019-03-13T06:59:28.746857Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/leon/TankZhou/env/python3.7-pytorch1.0.1/lib/python3.7/site-packages/pandas/plotting/_converter.py:129: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
      "\n",
      "To register the converters:\n",
      "\t>>> from pandas.plotting import register_matplotlib_converters\n",
      "\t>>> register_matplotlib_converters()\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 数据集建立\n",
    "train_data, val_data, test_data, all_series, df_index = getData(df, 'high', days_before=DAYS_BEFORE, days_pred=DAYS_PRED, train_end=TRAIN_END)\n",
    "\n",
    "# 获取所有原始数据\n",
    "all_series_test1 = np.array(all_series.copy().tolist())\n",
    "# 绘制原始数据的图\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(df_index, all_series_test1, label='real-data')\n",
    "\n",
    "# 归一化，便与训练\n",
    "train_data_numpy = np.array(train_data)\n",
    "train_mean = np.mean(train_data_numpy)\n",
    "train_std  = np.std(train_data_numpy)\n",
    "train_data_numpy = (train_data_numpy - train_mean) / train_std\n",
    "train_data_tensor = torch.Tensor(train_data_numpy)\n",
    "\n",
    "val_data_numpy = np.array(val_data)\n",
    "val_data_numpy = (val_data_numpy - train_mean) / train_std\n",
    "val_data_tensor = torch.Tensor(val_data_numpy)\n",
    "\n",
    "test_data_numpy = np.array(train_data)\n",
    "test_data_numpy = (test_data_numpy - train_mean) / train_std\n",
    "test_data_tensor = torch.Tensor(test_data_numpy)\n",
    "\n",
    "# 创建 dataloader\n",
    "train_set = TrainSet(train_data_tensor)\n",
    "train_loader = DataLoader(train_set, batch_size=256, shuffle=True)\n",
    "\n",
    "val_set = TrainSet(val_data_tensor)\n",
    "val_loader = DataLoader(val_set, batch_size=256, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-13T06:59:29.090522Z",
     "start_time": "2019-03-13T06:59:29.088312Z"
    }
   },
   "outputs": [],
   "source": [
    "# for tx, ty in train_loader:\n",
    "#     print(tx.shape)\n",
    "#     print(ty.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型训练部分\n",
    "\n",
    "这个部分如果是不想要训练的话（比如已经训练好了模型），替换为 rnn = torch.load('rnn.pkl') （记得把原来的注释掉）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-13T06:59:29.104166Z",
     "start_time": "2019-03-13T06:59:29.092431Z"
    }
   },
   "outputs": [],
   "source": [
    "# rnn = LSTM()\n",
    "\n",
    "# if torch.cuda.is_available():\n",
    "#     rnn = rnn.cuda()\n",
    "\n",
    "# optimizer = torch.optim.Adam(rnn.parameters(), lr=LR)  # optimize all cnn parameters\n",
    "# loss_func = nn.MSELoss()\n",
    "\n",
    "# best_loss = 1000\n",
    "\n",
    "\n",
    "# if not os.path.exists('weights'):\n",
    "#     os.mkdir('weights')    \n",
    "\n",
    "# for step in range(EPOCH):\n",
    "#     for tx, ty in train_loader:\n",
    "#         if torch.cuda.is_available():\n",
    "#             tx = tx.cuda()\n",
    "#             ty = ty.cuda() \n",
    "        \n",
    "#         output = rnn(torch.unsqueeze(tx, dim=2))             \n",
    "#         loss = loss_func(torch.squeeze(output), ty)        \n",
    "#         optimizer.zero_grad()  # clear gradients for this training step\n",
    "#         loss.backward()  # back propagation, compute gradients\n",
    "#         optimizer.step()\n",
    "        \n",
    "#         print('epoch : %d  ' % step, 'train_loss : %.4f' % loss.cpu().item())\n",
    "        \n",
    "#     with torch.no_grad():\n",
    "#         for tx, ty in val_loader:\n",
    "#             if torch.cuda.is_available():\n",
    "#                 tx = tx.cuda()\n",
    "#                 ty = ty.cuda() \n",
    "        \n",
    "#             output = rnn(torch.unsqueeze(tx, dim=2))             \n",
    "#             loss = loss_func(torch.squeeze(output), ty)\n",
    "        \n",
    "#             print('epoch : %d  ' % step, 'val_loss : %.4f' % loss.cpu().item())\n",
    "        \n",
    "#         if loss.cpu().item() < best_loss:\n",
    "#             best_loss = loss.cpu().item()\n",
    "#             torch.save(rnn, 'weights/rnn.pkl'.format(loss.cpu().item()))\n",
    "#             print('new model saved at epoch {} with val_loss {}'.format(step, best_loss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 画图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-13T06:59:31.915366Z",
     "start_time": "2019-03-13T06:59:29.106704Z"
    }
   },
   "outputs": [],
   "source": [
    "rnn = LSTM()\n",
    "\n",
    "rnn = torch.load('weights/rnn.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-13T06:59:32.640680Z",
     "start_time": "2019-03-13T06:59:31.917486Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "generate_data_train = []\n",
    "generate_data_test = []\n",
    "\n",
    "# 测试数据开始的索引\n",
    "test_start = len(all_series_test1) + TRAIN_END\n",
    "\n",
    "# 对所有的数据进行相同的归一化\n",
    "all_series_test1 = (all_series_test1 - train_mean) / train_std\n",
    "all_series_test1 = torch.Tensor(all_series_test1)\n",
    "\n",
    "# len(all_series_test1)  # 3448\n",
    "\n",
    "for i in range(DAYS_BEFORE, len(all_series_test1) - DAYS_PRED, DAYS_PRED):\n",
    "    x = all_series_test1[i - DAYS_BEFORE:i]\n",
    "    # 将 x 填充到 (bs, ts, is) 中的 timesteps\n",
    "    x = torch.unsqueeze(torch.unsqueeze(x, dim=0), dim=2)\n",
    "    \n",
    "    if torch.cuda.is_available():\n",
    "        x = x.cuda()\n",
    "\n",
    "    y = torch.squeeze(rnn(x))\n",
    "    \n",
    "    if i < test_start:\n",
    "        generate_data_train.append(torch.squeeze(y.cpu()).detach().numpy() * train_std + train_mean)\n",
    "    else:\n",
    "        generate_data_test.append(torch.squeeze(y.cpu()).detach().numpy() * train_std + train_mean)\n",
    "        \n",
    "generate_data_train = np.concatenate(generate_data_train, axis=0)\n",
    "generate_data_test  = np.concatenate(generate_data_test, axis=0)\n",
    "\n",
    "# print(len(generate_data_train))   # 3122\n",
    "# print(len(generate_data_test))    # 294\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(df_index[DAYS_BEFORE: len(generate_data_train) + DAYS_BEFORE], generate_data_train, 'b', label='generate_train')\n",
    "plt.plot(df_index[TRAIN_END:len(generate_data_test) + TRAIN_END], generate_data_test, 'k', label='generate_test')\n",
    "plt.plot(df_index, all_series_test1.clone().numpy() * train_std + train_mean, 'r', label='real_data')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-13T06:59:33.136823Z",
     "start_time": "2019-03-13T06:59:32.643742Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1152 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,16))\n",
    "\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(df_index[100 + DAYS_BEFORE: 130 + DAYS_BEFORE], generate_data_train[100: 130], 'b', label='generate_train')\n",
    "plt.plot(df_index[100 + DAYS_BEFORE: 130 + DAYS_BEFORE], (all_series_test1.clone().numpy()* train_std + train_mean)[100 + DAYS_BEFORE: 130 + DAYS_BEFORE], 'r', label='real_data')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(df_index[TRAIN_END + 50: TRAIN_END + 80], generate_data_test[50:80], 'k', label='generate_test')\n",
    "plt.plot(df_index[TRAIN_END + 50: TRAIN_END + 80], (all_series_test1.clone().numpy()* train_std + train_mean)[TRAIN_END + 50: TRAIN_END + 80], 'r', label='real_data')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-13T06:59:33.832117Z",
     "start_time": "2019-03-13T06:59:33.139843Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3448\n",
      "3448\n",
      "3446\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "generate_data_train = []\n",
    "generate_data_test = []\n",
    "\n",
    "all_series_test2 = np.array(all_series.copy().tolist())\n",
    "\n",
    "# 对所有的数据进行相同的归一化\n",
    "all_series_test2 = (all_series_test2 - train_mean) / train_std\n",
    "all_series_test2 = torch.Tensor(all_series_test2)\n",
    "\n",
    "iter_series = all_series_test2[:DAYS_BEFORE]\n",
    "\n",
    "index = DAYS_BEFORE\n",
    "\n",
    "while index < len(all_series_test2) - DAYS_PRED:\n",
    "    x = torch.unsqueeze(torch.unsqueeze(iter_series[-DAYS_BEFORE:], dim=0), dim=2)\n",
    "    \n",
    "    if torch.cuda.is_available():\n",
    "        x = x.cuda()\n",
    "\n",
    "    y = torch.squeeze(rnn(x))\n",
    "    \n",
    "    iter_series = torch.cat((iter_series.cpu(), y.cpu()))\n",
    "    \n",
    "    index += DAYS_PRED\n",
    "\n",
    "iter_series = iter_series.detach().cpu().clone().numpy() * train_std + train_mean\n",
    "\n",
    "print(len(all_series_test2))\n",
    "print(len(df_index))\n",
    "print(len(iter_series))\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(df_index[ : len(iter_series)], iter_series, 'b', label='generate_train')\n",
    "plt.plot(df_index, all_series_test2.clone().numpy() * train_std + train_mean, 'r', label='real_data')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-13T06:59:34.147720Z",
     "start_time": "2019-03-13T06:59:33.834718Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,16))\n",
    "\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(df_index[ 3000 : 3049], iter_series[3000:3049], 'b', label='generate_train')\n",
    "plt.plot(df_index[ 3000 : 3049], all_series_test2.clone().numpy()[3000 : 3049] * train_std + train_mean, 'r', label='real_data')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "py3.7-pt1.0",
   "language": "python",
   "name": "py3.7-pt1.0"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
